Automatic signal identification (ASI) is an essential part of intelligent radios used in various military and commercial applications, such as electronic warfare, spectrum surveillance and software-defined and cognitive radios.
ASI algorithms can be categorized into two main classes: likelihood-based and feature-based. While the former algorithms provide the maximum average probability of correct identification, they are complex to implement and sensitive to model mismatches. On the other hand, feature-based algorithms are in general simpler to implement and robust to model mismatches. However, their performance is sub-optimal. In the prior art, feature-based algorithms have typically been used, and then only to identify standard signals and most of them concern the identification of a single standard signal (i.e., signal versus noise, also known as signal detection). For the identification of cellular standard signals versus other signals, second-order cyclostationarity-based features have been used to identify IEEE 802.11 standard signals, Long Term Evolution downlink (LTE-DL) versus WiMAX signals, GSM versus LTE-DL signals, and GSM versus CDMA and orthogonal frequency division multiplexing (OFDM) signals. A wavelet-based algorithm has been used to identify GSM versus CDMA signals, with GSM employing Gaussian minimum-shift-keying (GMSK) modulation and CDMA using offset quadrature phase-shift-keying. However, the prior art in general requires long observation intervals, which may not be available in certain applications. For example, only a small portion of the jamming interval is assigned for signal captures in reactive jamming in order not to degrade jamming performance, which leaves only short durations for signal identification.
Accordingly, there is a need for improved cellular network identifications methods and/or systems.